In this project, I will try to predict race results from a wide
variety of predictors for the 2022 Mens World Tour in professional road
cycling. Road cycling is notoriously unpredictable so it will be
interesting to see how a machine learning algorithm tackles this
problem. Predictors will be divided into two general categories. The
first category is rider profile. Rider profile includes variables such
as rider age, rider weight, and rider ranking in a variety of different
strengths. In pro cycling, riders generally specialize. There are riders
who are designated sprinters. These riders have a very good 30 second
power but are generally larger and do not have good power over longer
ranges. This means that these riders are good at sprinting to a win in a
flat race but get dropped when there is a hill. There are other riders
who are very light and have good long power. These riders are excellent
at climbing but cannot compete against the sprinters. There are many
riders who specialize somewhere between a sprinter and a climber. The
other category of predictor is race profile. This includes attributes
such as race length, vertical meters covered, race ranking, and more.
Throughout the year, there are certain races that rank higher than other
races. For example, most people have heard of the Tour de France but
only intense cycling fans know of races such as the Bemer Classic. These
higher quality races have better riders starting at them and are thus
more prestigious to win raising the level of competition. A rider who
could do well at the Bemer Classic might struggle to do well at the Tour
de France. By combining rider profile and racer profile, I hope to
create an algorithm that is able to provide insights that are missed by
most cycling commentators. Since races are so unpredictable, there are
many variables that are difficult to quantify and are thus missing from
this analysis. For example, there is nothing to quantify what happened
in a race known as Strade Bianche in 2022 when strong winds blew half of
the competitors off of the course causing some spectacular
crashes.
(all riders were relatively ok) Let’s see how the models do.